class_docstr: torchaudio.models.RNNT()
  
  Recurrent neural network transducer (RNN-T) model.
  
  Note:
      To build the model, please use one of the factory functions.
  
  Args:
      transcriber (torch.nn.Module): transcription network.
      predictor (torch.nn.Module): prediction network.
      joiner (torch.nn.Module): joint network.
      attndim (int): TCPGen attention dimension
      biasing (bool): If true, use biasing, otherwise use standard RNN-T
      deepbiasing (bool): If true, use deep biasing by extracting the biasing vector
      embdim (int): dimension of symbol embeddings
      jointdim (int): dimension of the joint network joint dimension
      charlist (list): The list of word piece tokens in the same order as the output layer
      encoutdim (int): dimension of the encoder output vectors
      dropout_tcpgen (float): dropout rate for TCPGen
      tcpsche (int): The epoch at which TCPGen starts to train
      DBaverage (bool): If true, instead of TCPGen, use DBRNNT for biasing
code: def get_tcpgen_distribution(self, query, ptrdist_mask):
      # Make use of the predictor embedding matrix
      keyvalues = torch.cat(
          [self.predictor.embedding.weight.data, self.ooKBemb.weight],
          dim=0,
      )
      keyvalues = self.dropout_tcpgen(self.Kproj(keyvalues))
      # B * T * U * attndim, nbpe * attndim -> B * T * U * nbpe
      tcpgendist = torch.einsum("ntuj,ij->ntui", query, keyvalues)
      tcpgendist = tcpgendist / math.sqrt(query.size(-1))
      ptrdist_mask = ptrdist_mask.unsqueeze(1).repeat(
          1, tcpgendist.size(1), 1, 1
      )
      tcpgendist.masked_fill_(ptrdist_mask.bool(), -1e9)
      tcpgendist = torch.nn.functional.softmax(tcpgendist, dim=-1)
      # B * T * U * nbpe, nbpe * attndim -> B * T * U * attndim
      hptr = torch.einsum(
          "ntui,ij->ntuj", tcpgendist[:, :, :, :-1], keyvalues[:-1, :]
      )
      return hptr, tcpgendist
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 3 for calling the RNNTBiasing obj's underlying member method code with example usage: obj.get_tcpgen_distribution(query, ptrdist_mask) and returns: hptr,  tcpgendist